1. Field of the Invention
The present invention generally relates to super-resolution of noisy infrared imaging signals for the remote surveillance of objects such as vehicles or missiles through the fusion of radar and infrared technologies.
2. Description of the Related Art
Conventional infrared systems detect the naturally emitted or reflected electromagnetic radiation from objects (e.g., heat) with devices such as a charge-coupled device (CCD) array. However, conventional systems have good resolution only within a short range (e.g., a few kilometers) because devices such as CCD arrays have limited physical characteristics (e.g., a limited number of pixels) which restrict resolution at longer distances.
For example, an infrared image of a object which is close may be detected by a matrix of hundreds of pixels of a CCD array. To the contrary, an object which is at a great distance may only be detected by a few pixels. Such a distant object would appear as a small dot or blob on a monitor and its shape would not be large enough to be recognizable. For example, the Johnston criteria of minimum object size states that the smallest recognizable size occurs with a 3.times.6 array of 18 pixels. To overcome this problem algorithms were conventionally utilized to increase resolution.
Fourier Transform (FT) iterative algorithms were designed originally for electron microscope phase retrieval problems by Gerchberg and Saxton (e.g., see R. W. Gerchberg and W. O. Saxton, "A Practical Algorithm for the determination of phase from image and diffraction plane pictures," Optik Vol. 35, pp. 237-246, 1972 and W. O. Saxton, "Computer Techniques for Image Processing in Electron Microscopy" (Academic, New York, 1978)).
Subsequently, the Gerchberg and Saxton algorithm was extended to extrapolate signals and images successfully in ideal cases of weak or no noise (e.g., see J. R. Fienup, "Feature Issue on Signal Recovery," J. Opt. Soc. Am. Vol. 73, No. 11, pp. 1412-1526, November 1983; R. W. Gerchberg, "Super-resolution through error energy reduction," Opt. Acta Vol. 21, pp. 709-720, 1974; A. Papoulis, "A New Algorithm in Spectral Analysis and Band limited Extrapolation," IEEE Trans. Circuits Syst. CAS-22, p. 42, 1975; J. Cadzow, "An Extrapolation Procedure for Bandlimited Signals," IEEE Trans. Acoust. Speech Signal Process, ASSP-27, pp. 4-12, 1979; and J. L. Sanz and T. S. Huang, "Unified Hilbert Space Approach to Iterative Least-Squares Linear Signal Restoration," J. Opt. Soc. Am. Vol. 73, No. 11, pp. 1455-1465, November 1983.
However, when there is poor Signal-to-Noise Ratio (SNR), the performance of such a conventional system drastically deteriorates due to the ill-conditioned nature of such a noisy super-resolution. This makes real world applications impractical since a poor signal to noise ratio is common in real world applications.